Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.

Job losses, multinationals and globalization: the anatomy of disempowerment

Includes bibliography

Valuing damage and losses in cultural assets after a disaster: concept paper and research options

Includes bibliography

What is the importance of selecting features for non-technical losses identification?

Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.

Ammonia volatilization losses from surface-applied urea with urease and nitrification inhibitors

Urease inhibitor (UI) and nitrification inhibitor (NI) have the potential to improve N-use efficiency of applied urea and minimize N losses via gaseous emissions of ammonia (NH 3) to the atmosphere and nitrate (NO3-) leaching into surface and ground water bodies. There is a growing interest in the formulations of coating chemical fertilizers with both UI and NI. However, limited information is available on the combined use of UI and NI applied with urea fertilizer. Therefore the aim of this study was to investigate the effects of treating urea with both UI and NI to minimize NH 3 volatilization. Two experiments were set up in volatilization chambers under controlled conditions to examine this process. In the first experiment, UR was treated with the urease inhibitor NBPT [N-(n-butyl) thiophosphoric acid triamide] at a rate of 1060 mg kg -1 urea and/or with the nitrification inhibitor DCD (dicyandiamide) at rates equivalent to 5 or 10% of the urea N. A randomized experimental design with five treatments and five replicates was used: 1) UR, 2) UR + NBPT, 3) UR + DCD 10%, 4) UR + NBPT + DCD 5%, and 5) UR + NBPT + DCD 10%. The fertilizer treatments were applied to the surface of an acidic Red Latosol soil moistened to 60% of the maximum water retention and placed inside volatilization chambers. Controls chambers were added to allow for NH 3 volatilized from unfertilized soil or contained in the air that swept over the soil surface. The second experiment had an additional treatment with surface-applied DCD. The chambers were glass vessels (1.5 L) fit with air inlet and outlet tubings to allow air to pass over the soil. Ammonia volatilized was swept and carried to a flask containing a boric acid solution to trap the gas and then measured daily by titration with a standardized H 2SO 4 solution. Continuous measurements were recorded for 19 and 23 days for the first and second experiment, respectively. The soil samples were then analyzed for UR-, NH4+-, and NO3--N. Losses of NH 3 by volatilization with unamended UR ranged from 28 to 37% of the applied N, with peak of losses observed the third day after fertilization. NBPT delayed the peak of NH 3 losses due to urease inhibition and reduced NH 3 volatilization between 54 and 78% when compared with untreated UR. Up to 10 days after the fertilizer application, NH 3 losses had not been affected by DCD in the UR or the UR + NBPT treatments; thereafter, NH 3 volatilization tended to decrease, but not when DCD was present. As a consequence, the addition of DCD caused a 5-16% increase in NH 3 volatilization losses of the fertilizer N applied as UR from both the UR and the UR + NBPT treatments. Because the effectiveness of NBPT to inhibit soil urease activity was strong only in the first week, it could be concluded that DCD did not affect the action of NBPT but rather, enhanced volatilization losses by maintaining higher soil NH4+ concentration and pH for a longer time. Depending on the combination of factors influencing NH 3 volatilization, DCD could even offset the beneficial effect of NBPT in reducing NH 3 volatilization losses. © 2012 Elsevier Ltd.

Identification and feature selection of non-technical losses for industrial consumers using the software WEKA

This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. © 2012 IEEE.

CO2-C losses and carbon quality of selected Maritime Antarctic soils

Polar Regions are the most important soil carbon reservoirs on Earth. Monitoring soil carbon storage in a changing global climate context may indicate possible effects of climate change on terrestrial environments. In this regard, we need to understand the dynamics of soil organic matter in relation to its chemical characteristics. We evaluated the influence of chemical characteristics of humic substances on the process of soil organic matter mineralization in selected Maritime Antarctic soils. A laboratory assay was carried out with soils from five locations from King George Island. We determined the contents of total organic carbon, oxidizable carbon fractions of soil organic matter, and humic substances. Two in situ field experiments were carried out during two summers, in order to evaluate the CO2-C emissions in relation to soil temperature variations. The overall low amounts of soil organic matter in Maritime Antarctic soils have a low humification degree and reduced microbial activity. CO2-C emissions showed significant exponential relationship with temperature, suggesting a sharp increase in CO2-C emissions with a warming scenario, and Q10 values (the percentage increase in emission for a 10°C increase in soil temperature) were higher than values reported from elsewhere. The sensitivity of the CO2-C emission in relation to temperature was significantly correlated with the humification degree of soil organic matter and microbial activity for Antarctic soils. © 2012 Antarctic Science Ltd.

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.

Using baseline methods to identify non-technical losses in the context of smart grids

Demand response has gained increasing importance in the context of competitive electricity markets and smart grid environments. In addition to the importance that has been given to the development of business models for integrating demand response, several methods have been developed to evaluate the consumers' performance after the participation in a demand response event. The present paper uses those performance evaluation methods, namely customer baseline load calculation methods, to determine the expected consumption in each period of the consumer historic data. In the cases in which there is a certain difference between the actual consumption and the estimated consumption, the consumer is identified as a potential cause of non-technical losses. A case study demonstrates the application of the proposed method to real consumption data. © 2013 IEEE.

Guyana: socio-economic assessment of the damages and losses caused by the January-February 2005 flooding

Includes bibliography

Saint Lucia: macro socio-economic assessment of the damage and losses caused by hurricane Dean

Includes bibliography

Commonwealth of Dominica: socio-economic assessment of the damage and losses caused by hurricane Dean

Includes bibliography

Belize: macro socio-economic assessment of the damage and losses caused by hurricane Dean

Includes bibliography

Turks and Caicos Islands: macro socio-economic assessment of the damage and losses caused by tropical storm Hanna and hurricane Ike

Includes bibliography

Cayman Islands: macro socio-economic assessment of the damage and losses caused by hurricane Paloma

Includes bibliography